Genetic testing in psychiatry: State of the evidence

Chapter 36

Genetic testing in psychiatry: State of the evidence Chad A. Bousmana,b,c,d,e,h, Lisa C. Brownf, Ajeet B. Singhg, Harris A. Eyreg,h,i,j,k and Daniel J. M€ ullerl,m a

Department of Medical Genetics, University of Calgary, Calgary, AB, Canada, b Department of Psychiatry, University of Calgary, Calgary, AB, Canada,

c

Department of Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada, d Alberta Children’s Hospital Research Institute, Calgary, AB,

Canada, e Hotchkiss Brain Institute, Calgary, AB, Canada, f Myriad Neuroscience, Mason, OH, United States, g IMPACT SRC, School of Medicine, Deakin University, Geelong, VIC, Australia, h Innovation Institute, Texas Medical Center, Houston, TX, United States, i Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia, j Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia, k Brainstorm Lab for Mental Health Innovation, Stanford Medical School, Stanford University, Palo Alto, CA, United States, l Centre for Addiction and Mental Health, Campbell Family Research Institute, Toronto, ON, Canada, m Department of Psychiatry, University of Toronto, Toronto, ON, Canada

1 Introduction Since the completion of the Human Genome Project in 2003, the field of psychiatric genetics has grown exponentially, leading to increases in both the depth and breadth of knowledge about the genetic factors related to the development and treatment of psychiatric disorders. Although the true underlying genetic factors involved in most common psychiatric disorders (i.e., major depression, anxiety, bipolar, and schizophrenia) and their effective treatment remain to be fully elucidated, efforts to translate what has been discovered into clinical practice are underway. These translational efforts align with the precision medicine approach, which aims to customize healthcare based on an individual’s genetic, environmental, and lifestyle factors (National Research Council (U.S.). Committee on A Framework for Developing a New Taxonomy of Disease, 2011), and holds promise for better patient outcomes and wellness through precision prevention, diagnosis, and treatment of psychiatric disorders. Genetic testing offers one avenue from which more precise prevention, diagnosis, and treatment of psychiatric disorders could emerge. Given the now relatively low, and decreasing cost of genetic testing, cost-effectiveness is becoming more justifiable, and is likely to accelerate clinical use. However, for precision psychiatry to become a reality, a solid evidence base from which this “precision” can be derived is required. In this chapter, we provide a concise overview of the evidence for genetic testing in psychiatry, with a specific focus on the most common psychiatric disorders, rather than disorders that already have established genetic testing in place, such as neurodevelopmental (e.g., Fragile X syndrome, phenylketonuria) or neurodegenerative (e.g., Huntington’s disease) disorders that are often seen by psychiatrists. We will also identify the current gaps in knowledge related to genetic testing, and offer ways forward for research that, in part, will inform translation and adoption of genetic testing in the psychiatric clinical setting.

2 Types of genetic tests From an implementation perspective, genetic testing can be broadly classified as either clinical or direct-to-consumer. Both types of testing can be of high quality (i.e., reliable and valid) and provide useful information that, when used correctly, can affect patient outcomes (i.e., utility). However, only clinical genetic testing requires initiation and/or mediation of testing by a healthcare provider. Herein, we focus exclusively on clinical genetic testing, as the evidence base for directto-consumer testing (e.g., 23andMe) in psychiatry is severely limited. Within the clinical genetic testing classification, tests can be further classified by their function. A number of classifications have been defined (NIH, 2017), but the most relevant to psychiatry relate to the identification of “at-risk” individuals (predictive), assisting with diagnosis, or guiding optimal treatment. In the subsequent sections, we present the evidence base for each of these genetic testing types as they relate to psychiatry. Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00036-5 Copyright © 2020 Elsevier Inc. All rights reserved.

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Current evidence base

Quality evidence is vital to the future of genetic testing in psychiatry. Although a number of frameworks for evaluating the evidence base for genetic tests exist, consensus on the evidentiary requirements are lacking (Morrison & Boudreau, 2012). However, common to all frameworks is the need to establish analytical validity, clinical validity, and clinical utility. A valid test must provide an accurate result, and both analytical and clinical validity are measures of this accuracy. Analytical validity refers to how well the tests can detect the genetic variant(s) it intends to detect, and clinical validity refers to how well the variant(s) are related to the presence, absence, or risk of a particular outcome. Whereas, clinical utility is a measure of the applicability (e.g., efficacy, effectiveness) and feasibility (e.g., acceptability, efficiency, affordability) of the test for improving patient outcomes. In the following sections, particular attention is given to evidence related to the clinical validity and clinical utility of genetic tests, as data related to analytical validity is typically not published. However, genetic testing laboratories in the U.S., for example, must demonstrate analytical validity to receive certification under the Clinical Laboratory Improvement Amendments, and similar international certification standards, and as such, analytical validity is typically implied.

3.1 Genetic tests for identifying “at risk” individuals The ability to identify or screen for individuals “at risk” for psychiatric illness has obvious clinical and public health implications. The majority of research in this area has focused on singular or combinatorial nongenetic factors (e.g., neuroimaging, cognitive, clinical), and has recently led to the development of several online risk calculators for the psychotic spectrum (Cannon et al., 2016; Fusar-Poli et al., 2017) and bipolar (Hafeman et al., 2017) disorders. However, to our knowledge, no risk calculator or screening instrument includes genetic information other than family history of a psychiatric disorder, which is an indirect measure of genetic risk. One example of genetic research related to the identification of “at risk” individuals has focused on transition to psychosis among individuals already deemed to be at ultra-high risk due to family history, subthreshold symptomatology, and/ or declines in functioning. Among these ultra-high risk individuals, genetic variation in catechol-o-methyltransferase (COMT) (McIntosh et al., 2007), neuregulin 1 (NRG1) (Bousman et al., 2013; Hall et al., 2006; Keri, Kiss, & Kelemen, 2009), D-amino acid oxidase activator (DAOA) (Bousman et al., 2013; M€ossner et al., 2010), and interleukin 1 beta (IL1B) (Bousman, Lee, et al., 2017) have been associated with risk for transition to psychosis. However, the positive predictive values (i.e., probability that transition occurs when the allele is present) of the variants identified to date are either suboptimal, or have not been replicated, hindering their clear utility in clinical practice. Thus, the current evidence base for genetic testing for the purpose of identifying individuals “at risk” for a psychiatric disorder is extremely limited, and currently has limited clinical utility.

3.2 Genetic tests for assisting with diagnosis Classification of major psychiatric disorders depends on a set of criteria used to assign individuals to a diagnostic category based on relevant clinical similarities. The two major psychiatric disorder classification systems (DSM and ICD) are polythetic, in that two individuals may have the same phenotypic classification, but exhibit or report variations in characteristic symptoms in contrast to monothetic, in which the members would be identical in all characteristics (Millon, 1991). As such, a genetic test to assist with the process of assigning and/or ruling out a diagnosis could be of clinical value. The challenge, however, is that the common psychiatric disorders are genetically complex, following a polygenic inheritance pattern (i.e., caused by multiple genes) in which single variants associated with a disorder have poor penetrance and lack diagnostic specificity. In fact, the Psychiatric Genomics Consortium (PGC) has shown that common genetic variation explains, at best, about 7% of the variation in diagnosis liability (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), and the Cross-Disorder Group of PGC has shown high to moderate genetic correlation among most of the common psychiatric disorders (Lee et al., 2013). Thus, it may not be surprising that genetic-based risk classifiers for autism (Pramparo et al., 2015; Skafidas et al., 2014), schizophrenia (Liu et al., 2017), bipolar (Chuang & Kuo, 2017), and depression (Wong, Dong, Andreev, Arcos-Burgos, & Licinio, 2012) have not demonstrated strong evidence for translation into clinical settings, and as such, the prospects for common genetic variation to be useful in aiding diagnosis in the near future are slim. An argument could be made for the use of rare copy number variants (CNVs) in assisting with diagnosis. CNVs result in chromosomal microdeletions or microduplications, and can substantially increase risk for a psychiatric disorder (Malhotra & Sebat, 2012). However, similar to common genetic variants, they lack a strong degree of diagnostic specificity, and as such are likely to have limited utility. However, the Genetic Testing Working Group of the International Society of

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Psychiatric Genetics has recommended that certain CNVs may help diagnose rare conditions that have important medical and psychiatric implications for individual patients, and may inform family counseling, particularly if there is a strong family history of mental illness (ISPG, 2017). Overall, genetic testing for the purpose of assisting with diagnosis is not fully ready for use in psychiatry, with few exceptions (e.g., DiGeorge syndrome). However, as the field of psychiatric genetics continues to evolve, the availability of exome and whole genome sequencing data reaches a critical mass, the quality and scale of phenotype data within electronic health records increases, digital phenotyping techniques improve, and methodological/statistical approaches advance, there will likely be opportunities for genetic/genomic testing to be used in tandem with traditional diagnostic classification approaches.

3.3 Genetic tests to guide optimal therapy Commercially, rapidly growing, and arguably the most promising use of genetic testing in psychiatry is for guiding the selection and/or dosing of pharmacological therapies, also known as pharmacogenetic testing. Eric Green, director of the U.S. National Human Genome Research Institute, labeled genotype-guided prescribing as one of four “low-hanging fruits” for genomic uptake in the clinic (Green & Guyer, 2011), and the NIH Director, Francis Collins, has predicted this uptake will happen by 2020 (Collins & McKusick, 2001). The likelihood, however, of these assertions becoming reality are debatable, and have recently been the topic of thoughtful commentary that contain both optimism and skepticism (Abbasi, 2016; de Leon & Spina, 2016; Drew, 2016; Dubovsky, 2016; Rosenblat, Lee, & McIntyre, 2017). Response to pharmacological therapy is known to be polygenic and genetically distinct from the disorders these therapies were developed to treat (Garcı´a-Gonza´lez et al., 2017). The molecular mechanisms and associated genes related to pharmacological response are classically categorized as either pharmacokinetic or pharmacodynamic. Pharmacokinetic genes are those that encode proteins involved in the absorption, distribution, metabolism, and excretion of medications. Whereas, pharmacodynamic genes encode proteins involved in neurotransmitter synthesis, reception, transport, and degradation associated with the actions of medications. A number of resources are available to assist in the evaluation of the evidence linking these pharmacokinetic and pharmacodynamic genes to medication outcomes (Table 1). Utilizing these resources, one can derive a reasonable indication of the current evidence for any gene-drug pair. It is beyond the scope of this chapter to present the evidence for every genedrug pair relevant to psychiatry, but we have highlighted the gene-drug pairs with the strongest evidence base to date in the following sections.

3.3.1 HLA-B/HLA-A—Carbamazepine The U.S. Food and Drug Administration (FDA) requires testing for the presence of the HLA-B*15:02 allele prior to prescribing carbamazepine, and recommends testing for this allele prior to oxcarbazepine prescribing (Ferrell & McLeod, 2008) in at-risk populations (i.e., patients of Chinese and Southeast Asian descent). This advice is based on the increased risk of rare but serious cutaneous adverse reactions (e.g., Stevens-Johnson syndrome and toxic epidermal necrolysis) among carriers of the HLA-B*15:02 allele following exposure to carbamazepine, and to a lesser extent, oxcarbazepine (Yip, Marson, Jorgensen, Pirmohamed, & Alfirevic, 2012). In addition to the HLA-B*15:02 allele, Health Canada also recommends testing for the HLA-A*31:01 allele prior to carbamazepine prescribing. Aligned with these recommendations, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Canadian Pharmacogenomics Network for Drug Safety (CPNDS) have developed guidelines for using HLA-B genetic information when prescribing carbamazepine. The CPNDS has also included the HLA-A*31:01 allele in their guidelines (Amstutz et al., 2014; Leckband et al., 2013), and CPIC’s updated guideline will include this allele (Phillips et al., unpublished). Notably, the highest incidence of the HLA-B*15:02 allele occurs among individuals of Chinese and Southeast Asian ancestry (Leckband et al., 2013), whereas the HLA-A*31:01 allele is more prevalent worldwide (Yip & Pirmohamed, 2017).

3.3.2 CYP2D6/CYP2C19—Antidepressants Although testing is not required or recommended by the FDA, they have included “actionable” information on numerous antidepressant labels related to evidence suggesting links between poor patient outcomes (adverse event and/or efficacy) and genetic variation in two cytochrome P-450 genes (CYP2D6 and CYP2C19) (Drozda, M€uller, & Bishop, 2014). These phase I hepatic metabolism enzyme coding genes have numerous functional polymorphisms that vary widely among individuals and populations. Genetic variations (e.g., deletions, duplications, point mutations) in these two genes can result in abolished, reduced, normal, or enhanced enzyme activity (Padmanabhan, 2014). Based on these genetic variations, an individual’s metabolic phenotype can be predicted. Individuals that carry two alleles that code for “normal” enzyme function

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TABLE 1 Gene-drug interaction evidence, dosing guideline, and drug label resources.

Resource

Website

PharmGKB

pharmgkb.org

CPIC

DPWG

CPNDS

cpicpgx.org

pharmgkb.org/ page/dpwg

cpnds.ubc.ca

Evidence levels

Level description

Level 1 A/B— High

(1A) Annotation for a variant-drug combination in a CPIC or medical societyendorsed PGx guideline, or implemented at a PGRN site or in another major health system. (1B) Annotation for a variant-drug combination where the preponderance of evidence shows an association. The association must be replicated in more than one cohort with significant p-values, and preferably will have a strong effect size.

Level 2 A/B— Moderate

Annotation for a variant-drug combination with moderate evidence of an association. The association must be replicated but there may be some studies that do not show statistical significance, and/or the effect size may be small. The variants in level 2A are in known pharmacogenes, so functional significance is more likely.

Level 3—Low

Annotation for a variant-drug combination based on a single significant (not yet replicated) study or annotation for a variant-drug combination evaluated in multiple studies, but lacking clear evidence of an association.

Level 4— Preliminary

Annotation based on a case report, nonsignificant study, or in vitro, molecular, or functional assay evidence only.

A

Genetic information should be used to change prescribing of affected drug.

B

Genetic information could be used to change prescribing of the affected drug because alternative therapies/dosing are extremely likely to be as effective and as safe as nongenetically based dosing.

C

There are published studies at varying levels of evidence, some with mechanistic rationale, but no prescribing actions are recommended because (a) dosing based on genetics makes no convincing difference, or (b) alternatives are unclear, possibly less effective, more toxic, or otherwise impractical, or (c) few published studies or mostly weak evidence and clinical actions are unclear.

D

There are few published studies, clinical actions are unclear, little mechanistic basis, mostly weak evidence, or substantial conflicting data. If the genes are not widely tested for clinically, evaluations are not needed.

4

Published controlled studies of “good quality” relating to phenotyped and/or genotyped patients or healthy volunteers, and having relevant pharmacokinetic or clinical endpoints. “Good quality” criteria include (i) the use of concomitant medication with a possible effect on the phenotype is reported in the manuscript; (ii) confounders are reported (e.g., smoking status); (iii) the reported data are based on steady-state kinetics; and (iv) results are corrected for dose variability.

3

Published controlled studies of “moderate quality” relating to phenotyped and/or genotyped patients or healthy volunteers, and having relevant pharmacokinetic or clinical endpoints.

2

Published case reports, well documented, and having relevant pharmacokinetic or clinical endpoints. Well documented case series.

1

Published incomplete case reports. Product information.

0

Data on file

???

No evidence

N/A

Guidelines only. Focus on adverse drug reactions in pediatric populations.

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TABLE 1 Gene-drug interaction evidence, dosing guideline, and drug label resources.—cont’d

Resource

Website

FDA EMA PMDA HCSC

fda.gov ema.europa. eu/ema/ pmda.go.jp/ english/ canada.ca/en/ healthcanada/ services/ drugs-healthproducts.html

Evidence levels

Level description

Testing required

The label states or implies that some sort of gene, protein or chromosomal testing, including genetic testing, functional protein assays, cytogenetic studies, etc., should be conducted before using this drug. This requirement may only be for a particular subset of patients. Labels that state the variant is an indication for the drug, as implying a test requirement. If the label states a test “should be” performed, this is also interpreted as a requirement.

Testing recommended

The label states or implies that some sort of gene, protein or chromosomal testing, including genetic testing, functional protein assays, cytogenetic studies, etc., is recommended before using this drug. This recommendation may only be for a particular subset of patients. Labels that say testing “should be considered” to be recommending testing.

Actionable

The label does not discuss genetic or other testing for gene/protein/chromosomal variants, but does contain information about changes in efficacy, dosage or toxicity due to such variants. The label may mention contraindication of the drug in a particular subset of patients but does not require or recommend gene, protein or chromosomal testing.

Informative

The label mentions a gene or protein is involved in the metabolism or pharmacodynamics of the drug, but there is no information to suggest that variation in these genes/proteins leads to different response.

CPIC, Clinical Pharmacogenetics Implementation Consortium; CPNDS, Canadian Pharmacogenomics Network for Drug Safety; DPWG, Dutch Pharmacogenetics Working Group; EMA, European Medicines Agency; FDA, US Food and Drug Administration; HCSC, Health Canada (Sante Canada); PGRN, Pharmacogenomics Research Network; PGx, Pharmacogenetics; PharmGKB, Pharmacogenomics Knowledgebase; PMDA, Pharmaceuticals and Medical Devices Agency, Japan.

are classified as extensive or normal metabolizers, while those carrying two alleles that code for inactive or absent enzyme function are defined as poor metabolizers. Furthermore, individuals who carry one normal and one inactive/absent allele are classified as intermediate metabolizers, and those with a gain of function allele, gene duplications and/or multiplications of active alleles are defined as “rapid” or “ultrarapid” metabolizers. However, it is worth noting that genotype-predicted “normal” or “intermediate” metabolism can be converted to phenotypically “poor,” “rapid,” or “ultrarapid” metabolism by factors other than genotype (e.g., co-medications, co-morbidities, diet, smoking), an often underappreciated phenomenon known as phenoconversion (Shah & Smith, 2015). The bulk of the evidence between these two P-450 genes and antidepressant dosing is linked to selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs), although evidence is emerging for serotoninnorepinephrine reuptake inhibitors (SNRIs). This evidence has prompted the development of dosing guidelines for TCAs and SSRIs by the FDA, Health Canada, CPIC (Hicks et al., 2015; Hicks et al., 2016), and the Royal Dutch Association for the Advancement of Pharmacy—Pharmacogenetics Working Group (DPWG) (Swen et al., 2011), the latter guidelines including the SNRI, venlafaxine. However, it should be noted that the level of evidence according to the PharmGKB (Table 1) for venlafaxine is Level 2B, whereas all other antidepressants with guidelines have Level 1 evidence. Thus, one should not assume the evidence base for any two gene-drug interactions are equivalent based solely on the existence of dosing guidelines. Likewise, it should not be assumed that gene-drug pairs covered by two or more guidelines will provide the same guidance, particularly for pairs involving CYP2D6, where consensus is lacking across different consortia/expert groups. For example, a clinician intending to prescribe amitriptyline to an individual genotyped as CYP2D6 *10/*10 would be guided to give a normal starting dose based on the CPIC guidelines, but if consulting the DPWG guidelines, a 40% dose reduction would be recommended (Hicks, Swen, & Gaedigk, 2014). On the other hand, if an individual had a CYP2C19 genotype of *1/*17, CPIC would recommend an alternative therapy; whereas DPWG guidelines would advise the normal starting dose for amitriptyline (Bank et al., 2017).

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3.3.3 CYP2D6—Antipsychotics Relative to antidepressants, the pharmacogenetics of antipsychotic medications are less pronounced. However, the FDA, Health Canada, European Medicines Agency, and DPWG have deemed the level of evidence among CYP2D6 and several antipsychotics (aripiprazole, haloperidol, risperidone, and zuclopenthixol) sufficient to develop dosing guidelines (Swen et al., 2011). Other guideline groups (i.e., CPIC, CPNDS) have not followed suit, although it is expected that CPIC will be drafting similar guidelines in the near future. Interestingly, the PharmGKB level of evidence for interactions among CYP2D6 and these four antipsychotics are low (level 3), with the exception of risperidone (level 2A). This is not to say that existing guidelines are not justified, but it does suggest that different evidence thresholds are likely used to develop guidelines across groups/consortia, and as such, guidelines published by these groups may not be equivalent or interchangeable.

3.3.4 Promising gene-drug interactions There are a number of promising gene-drug pairs relevant to psychiatry that do not currently carry testing recommendations and/or appear in guidelines, but have an evidence base that suggests they may in the future. Table 2 provides a list of these gene-drug pairs, along with a summary of the current evidence. Of note, all of the gene-drug pairs mentioned in Table 2 were selected based on the presence of a moderate (level 2B) or higher level of evidence according to PharmGKB, and several of these pairs (denoted with “*” in Table 2) are currently being considered for guideline development by CPIC.

3.3.5 Commercial pharmacogenetic testing Numerous commercial pharmacogenetic-based decision support tools have emerged based on the pharmacogenetic evidence highlighted herein, and the well documented challenges clinicians face in medication selection and dosing (Bousman & Hopwood, 2016). These tools vary in the gene-drug pairs that are tested, but all include CYP2D6 and CY2C19; aligned with FDA, Health Canada, CPIC, and DPWG guidelines associated with these genes, and antidepressant/antipsychotic selection and dosing. However, it should be noted that the CYP2D6 and CY2C19 alleles tested by these commercial panels varies substantially, and as such, are not necessarily equivalent (Bousman, Jaksa, & Pantelis, 2017). In addition, only a minority (<20%) of commercial gene panels in psychiatry test for HLA-B*15:02 and/or HLA-A*31:01 alleles (Bousman & Hopwood, 2016), despite requirements/recommendations for testing, and the availability of international guidelines. Further, more than half (54%) of genes included on many of these testing panels have limited or preliminary evidence for their clinical utility in psychiatry (Fig. 1), and the medication recommendation algorithms that genotyping results are subjected to differ among companies. That said, results from four published small randomized controlled trials (RCTs) suggest the use of this testing to guide medication decisions in practice can improve patient outcomes (Bradley et al., 2017; Perez et al., 2017; Singh, 2015; Winner, Carhart, Altar, Allen, & Dechairo, 2013), particularly among individuals with moderate-severe major depressive disorder. Moreover, a number of open-label and retrospective studies have echoed the results from the RCTs (for review see: Peterson et al., 2017), and there is preliminary evidence suggesting these commercial tools could be cost-effective (Berm et al., 2016; Brown, Lorenz, Li, & Dechairo, 2017; Rosenblat et al., 2017). However, the evidence base for these tools is still emerging, and independent (noncommercial) validation of study findings has yet to be established. In addition, there are no professional or clinical guidelines to assist clinicians who are contemplating the use of these tools in their practice, and the availability of such testing may be limited depending on the region of the world in which a clinician’s practice operates.

4

Strategies for strengthening the evidence base

The current evidence base supporting genetic testing in psychiatry suggests testing is not ready for “prime-time,” with the exception of pharmacogenetic testing in specific clinical situations. As such, in the following section, we highlight a few strategies that will assist the field in generating the evidence required for genetic testing to move toward wide clinical adoption. We pay particular attention to pharmacogenetic testing given the availability and growing use of this testing in psychiatry.

4.1 Phenotype definition and measurement The Achille’s heel in psychiatric research is often the adequate definition and measurement of the phenotype of interest. Whether the phenotype of interest is “high-risk status,” diagnosis, or treatment response, there are considerable variations in how investigators can define and measure these phenotypes, which can result in heterogeneity within a sample,

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TABLE 2 Promising gene-drug interactions. Clinical

Evidence level

Drug labels

Association type

PharmaGKB

CPIC

FDA

EMA

PMDA

HCSC

CYP2D6— Atomoxetinea

Efficacy/toxicity

2A

B

Actionable

–

Actionable

Actionable

CYP2B6— Methadonea

Dosage

2A

B

–

–

–

–

CYP2D6— Risperidonea

Pharmacokinetic

2A

B

Informative

–

–

Informative

DRD2— Risperidone

Efficacy

2A

C

–

–

–

–

CYP2D6— Venlafaxine

Efficacy/toxicity

2A

B

Informative

–

–

–

SLC6A4— Citalopram/ escitalopram

Efficacy

2A

B/C

–

–

–

–

HTR2A— Antidepressants

Efficacy

2B

D

–

–

–

–

HTR2C— Antipsychotics

Toxicity

2B

D

–

–

–

–

FKBP5— Antidepressants

Efficacy

2B

D

–

–

–

–

DRD2/ANKK1— Antipsychotics

Toxicity

2B

D

–

–

–

–

MC4R— Antipsychotics

Toxicity

2B

C

–

–

–

–

GRIK4— Antidepressants

Efficacy

2B

D

–

–

–

–

ABCB1— Methadone

Dosage

2B

C/D

–

–

–

–

SCN1A— Carbamazepine

Dosage

2B

B

–

–

–

–

EPHX1— Carbamazepine

Dosage

2B

D

–

–

–

–

COMT/TXNRD2— SSRIs

Efficacy

2B

C

–

–

–

–

Gene-drug pair

CPIC, Clinical Pharmacogenetics Implementation Consortium; EMA, European Medicines Agency; FDA, US Food and Drug Administration; HCSC, Health Canada (Sant e Canada); PGRN, Pharmacogenomics Research Network; PGx, Pharmacogenetics; PharmGKB, Pharmacogenomics Knowledgebase; PMDA, Pharmaceuticals and Medical Devices Agency, Japan. a CPIC guidelines expected in the future.

potentially concealing underlying biological mechanisms and making comparisons between studies difficult. Although it would be naive to believe that all investigators will agree to use the same definitions and measures for a particular phenotype, we feel it is reasonable for a consensus to be developed on the minimum “caseness” criteria, and measurements required for a particular phenotype, without necessarily advocating for the exclusive use of any one measure or measurement procedure. Consensus efforts such as remission criteria in schizophrenia (Andreasen et al., 2005), antidepressant response and remission in depression (Riedel et al., 2010), and more recently, the diagnosis of treatment-resistance

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FIG. 1 Proportion of genes included on commercial pharmacogenetic testing panels that meet each of the four PharmGKB levels of evidence criteria. Data derived from Bousman, C. A., & Hopwood, M. (2016). Commercial pharmacogenetic-based decision-support tools in psychiatry. Lancet Psychiatry, 3(6), 585–590. https://doi.org/10.1016/S2215-0366(16)00017-1.

schizophrenia (Howes et al., 2017) are good examples. However, the degree to which these consensus criteria are adopted by investigators varies, and as a result, can hinder cross-comparison of research studies, and ultimately the strength of the evidence base.

4.2 Pharmacogenetic testing panel and reporting standardization A specific concern for current pharmacogenetic testing is the lack of standardization from the perspective of both gene panel content and results reporting. Panels range in size from one to 30 genes, and even when the same gene is included between two or more panels, the specific genetic variant tested within that gene may differ (Bousman & Hopwood, 2016; Bousman, Jaksa, et al., 2017). Due to these gene panel differences, the probability of inter-test agreement for a particular patient is modest, and as such, these tests are not interchangeable. Thus, the selection of which pharmacogenetic test to use is not trivial. Fortunately, recommendations for pharmacogenetic testing results reporting have recently been developed (Caudle et al., 2017; Kalman et al., 2016), but similar recommendations for gene panel membership do not exist. Based on the current evidence, it would seem reasonable to recommend the inclusion of CYP2D6, CYP2C19, HLA-A, and HLA-B on all testing panels. However, recommending which alleles (and allele phenotype calls) should be included for each of these genes, particularly CYP2D6 and CYP2C19, will require the convening of an expert panel to arrive at consensus.

4.3 Innovative clinical trials While implementation of pharmacogenetic testing in clinical practice has been demonstrated to be feasible at large-scale (Herbert et al., 2018; M€ uller, Kekin, Kao, & Brandl, 2013), there is debate on how best to assess the clinical validity and utility of pharmacogenetic-based decision support tools. One side has argued that rigorous double-blind RCTs are required ( Janssens & Deverka, 2014), while an emerging alternative side has argued that despite RCTs being viewed as the “goldstandard,” they are not particularly appropriate, because internal validity (i.e., experimental rigor) is maximized at the expense of external validity (i.e., clinical generalizability) (Dhanda, Guzauskas, Carlson, Basu, & Veenstra, 2017; Frieden, 2017; Gillis & Innocenti, 2014; Ratain & Johnson, 2014). Although there is certainly a need for RCTs to demonstrate the clinical validity of pharmacogenetic testing, they are not ideal for establishing clinical utility or usefulness. To determine clinical utility, trial designs must evaluate these tests in a manner that reflects how they will be used in the typical clinical setting (Miller, 2006). Good clinical practice would recommend patients are not blinded, and that treatment decisions are discussed between providers and patients as part of a shared decision making process (Arandjelovic et al., 2017). Thus, there is a need for pragmatic clinical trials with sufficient follow-up (i.e., 6 months or more) that can maximize external validity while maintaining sufficient internal rigor; approximating the “real world” implementation of pharmacogenetic testing (Frieden, 2017). Related to trial design, it is also evident that previous and ongoing trials of pharmacogenetic testing have a number of biases that need to be addressed in the future. First, all trials have been conducted by the manufacturers of the tests, and as such, independent evaluation is desperately needed. Second, the trials to date have included primarily middle-aged, depressed females of European descent (Peterson et al., 2017). Inclusion of men and ethnic minorities are needed in future trials to determine generalizability of testing in these populations. This is particularly important for pharmacogenetic

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testing because the allelic frequencies of most pharmacokinetic and pharmacodynamic genes vary widely by ethnicity (Gunes & Dahl, 2008; Ozawa et al., 2004; Van Booven et al., 2010); reducing or increasing the relevance of some alleles included on current testing panels. Third, there is currently no “gold standard” pharmacogenetic test in psychiatry. Head-tohead comparative trials will be required to determine superiority of a particular tool. However, given the large number of tools available, and the lack of any previous or ongoing head-to-head trials, it is unlikely that a gold standard will be determined in the short-term. Finally, there is a need for quality cost-effectivene analyses to be imbedded into future trials of pharmacogenetic testing. If clinical utility is favorable, but cost-effectiveness cannot be established, payers are less likely to approve the use of pharmacogenetic testing. Initial economic studies have been promising, but the quality of these studies has been questioned (Rosenblat et al., 2017). Encouragingly, there are at least six trials (NCT02466477, NCT02286440, NCT02189057, NCT02770339, NCT03113890, NCT02634177) underway that will, in part, address the preceding issues. These trials involve independent investigators, pragmatic design elements, diverse ethnic populations, multiple sites, and/ or economic outcome measures.

5 Conclusion There are formidable challenges that must be overcome prior to the global use of genetic testing in psychiatry. The identification of individuals “at-risk” for, and the diagnosis of, psychiatric disorders are not likely to benefit from predictive or diagnostic genetic testing in the immediate future. However, the use of pharmacogenetic tests to optimize pharmacotherapy shows promise. The challenges of implementing these tests in clinical practice are many (see Chapter 37; Liu et al., for detailed discussion), but the success of implementation efforts will ultimately depend on the strength of the underlying evidence base.

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